Supervised and unsupervised machine learning approaches for prediction and geographical discrimination of Iranian saffron ecotypes based on flower-related and phytochemical attributes

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY
Seid Mohammad Alavi-Siney , Jalal Saba , Alireza Fotuhi Siahpirani , Jaber Nasiri
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引用次数: 0

Abstract

A two-year field experiment (2014–2016; Zanjan, Iran) was conducted to monitor potential diversity pattern and adaptability power among 18 Iranian saffron ecotypes under Zanjan climatological conditions using seven flower-related and three qualitative traits (crocin, picrocrocin, and safranal, determined by UV–visible spectra), and analyzed by supervised and unsupervised approaches. A range of variability was recorded among the ecotypes, and despite some exceptions, overall, saffron corms produced higher amounts of studied features across the second year. The Feizabad ecotype was recommended to acquire maximum qualitative criteria (category I; based on ISO Normative 3632 grading system), while for flower-related parameters several ecotypes (e.g., Ghaien, Bardeskan, Torbat-Jam, and Gonabad) could be applied for Zanjan climatological conditions. Based on the results of Leave-One-Out Cross-Validation (LOOCV), various prediction values were computed for all 10 classifiers of LDA, QDA, FDA, MDA, RDA, Naive Bayes, Decision Tree, Linear SVM, Radial SVM, and Random Forest in terms of accuracy, sensitivity and specificity parameters. Among which, Random Forest and LDA with the values of 0.91 and 0.78 possessed the highest and the lowest amounts of accuracy, respectively. Finally, considering the highest accuracy value of the superior classification model of Random forest, both feature subsets of “FFW, FDW, Picrocrocin, Safranal, and Crocin” and “SFW, FDW, Picrocrocin, Safranal, and Crocin” were nominated as the most powerful elements (comparing to the remaining 1021 feature subsets) to make accurate discrimination between Khorasan and non-Khorasan saffron ecotypes. The results, overall, revealed that saffron ecotypes followed different responses under Zanjan climatological circumstances, and Random Forest is more suitable for accurately predicting saffron corms from different provenances.
基于花卉相关属性和植物化学属性的伊朗藏红花生态型预测和地理鉴别的有监督和无监督机器学习方法
为期两年的野外实验(2014-2016;利用7个花相关性状和3个质量性状(藏红花素、微番红花素和番红花素,由紫外可见光谱测定),对伊朗18个藏红花生态型在赞詹气候条件下的潜在多样性格局和适应能力进行了监测,并采用监督和非监督方法进行了分析。在生态型中记录了一系列的变化,尽管有一些例外,总的来说,藏红花球茎在第二年产生了更多的研究特征。Feizabad生态型被推荐获得最高的质量标准(第一类;基于ISO标准3632分级系统),而对于与花相关的参数,几个生态型(例如,Ghaien, Bardeskan, Torbat-Jam和Gonabad)可以适用于赞詹的气候条件。基于LOOCV交叉验证结果,计算LDA、QDA、FDA、MDA、RDA、朴素贝叶斯、决策树、线性支持向量机、径向支持向量机和随机森林10种分类器在准确率、灵敏度和特异性参数方面的预测值。其中Random Forest和LDA的准确率最高,分别为0.91和0.78。最后,考虑到随机森林优势分类模型的最高准确率值,将“FFW, FDW, Picrocrocin, Safranal, and Crocin”和“SFW, FDW, Picrocrocin, Safranal, and Crocin”两个特征子集提名为准确区分呼罗珊和非呼罗珊藏红花生态类型的最强大元素(与其余1021个特征子集相比)。结果表明,在赞詹气候条件下,藏红花生态型表现出不同的响应,随机森林更适合于对不同种源藏红花球茎的准确预测。
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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